exp_trials = rbind(exp_trials2, exp_trials3, exp_trials4)
exp_trials$prob = factor(exp_trials$prob)
ggplot(data = exp_trials) +
geom_boxplot(mapping = aes(x = prob, y = prob_rating, fill = prob))
mood2 = format_mood(read.csv("../data/04_exp_away_cond2-mood_ratings.csv"))
mood2$Answer.condition = "Confident"
mood3 = format_mood(read.csv("../data/04_exp_away_cond3-mood_ratings.csv"))
mood3$Answer.condition = "Pessimist"
mood4 = format_mood(read.csv("../data/04_exp_away_cond4-mood_ratings.csv"))
mood4$Answer.condition = "Cautious"
mood3$workerid = mood3$workerid + max(mood2$workerid) + 1
mood4$workerid = mood4$workerid + max(mood3$workerid) + 1
mood_all = rbind(mood2, mood3, mood4)
mood1_all = mood_all %>%
filter(type == "mood1") %>%
mutate(mood1 = mood_rating) %>%
mutate(mood_rating = NULL) %>%
mutate(type = NULL)
mood2_all = mood_all %>%
filter(type == "mood2") %>%
mutate(mood2 = mood_rating) %>%
mutate(mood_rating = NULL) %>%
mutate(type = NULL)
mood_all = merge(mood1_all, mood2_all)
mood_by_participant = mood_all
mood_by_participant$diff = mood_all$mood2 - mood_all$mood1
moodp1 = ggplot(data = mood_by_participant) +
geom_bar(mapping = aes(x = workerid, y = diff, fill = Answer.condition), stat = "identity")
moodp1
exclude_random = function(d) {
d_overall_means = d %>%
group_by(modal, workerid) %>%
summarise(rating_m_overall = mean(rating))
d_indiv_means = d %>%
group_by(modal,percent_window, workerid) %>%
summarise(rating_m = mean(rating))
d_indiv_merged = merge(d_indiv_means, d_overall_means, by=c("workerid", "modal"))
cors = d_indiv_merged %>%
group_by(workerid) %>%
summarise(corr = cor(rating_m, rating_m_overall))
exclude = cors %>%
filter(corr > 0.75) %>%
.$workerid
print(paste("Excluded", length(exclude), "participants based on random responses."))
d = d %>% filter(!(workerid %in% exclude))
}
d2 = exclude_random(d2)
## [1] "Excluded 11 participants based on random responses."
d3 = exclude_random(d3)
## [1] "Excluded 9 participants based on random responses."
d4 = exclude_random(d4)
## [1] "Excluded 14 participants based on random responses."
## Individual plots
plot(ps2$by_participant)
plot(ps3$by_participant)
plot(ps4$by_participant)
In the first t-test below, we see whether or not the difference in the AUC for the cautious speaker is significantly greater than that of the confident speaker. This allows us to see whether or not the speakers adapt to a specific speaker’s modal usage during the exposure phase.
##
## Two Sample t-test
##
## data: aucs.confident$auc_diff and aucs.cautious$auc_diff
## t = -5.1755, df = 133, p-value = 8.188e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -21.21955 -9.48492
## sample estimates:
## mean of x mean of y
## 1.615644 16.967878
In this second t-test, we see whether the difference in the AUC for the pessimistic speaker is significantly less than that of the cautious speaker. This allows us to see whether or not explaining-away has occurred.
##
## Two Sample t-test
##
## data: aucs.pessimist$auc_diff and aucs.cautious$auc_diff
## t = -2.3787, df = 135, p-value = 0.01878
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.801590 -1.454392
## sample estimates:
## mean of x mean of y
## 8.339888 16.967878